Slipbox MCP Server
Slipbox MCP Server

Dale a tu asistente de IA un papel activo en la gestión de tu conocimiento. Slipbox es un servidor MCP que convierte a cualquier agente compatible con MCP en un compañero Zettelkasten -- crea notas atómicas, forma enlaces semánticos, detecta clústeres emergentes y sintetiza ideas a partir de tu conocimiento existente.
Tus ideas entran, conocimiento estructurado sale. El agente se encarga del formato, los enlaces y la integración.
¿Nuevo en el método? Empieza con Introducción al método Zettelkasten para conocer el porqué de las notas atómicas y el pensamiento enlazado. Para ver cómo Slipbox prepara a tu agente con ese método, lee las instrucciones del servidor que envía automáticamente al conectarse.
Construido y probado con Claude. Funciona con cualquier cliente MCP (Claude Desktop, Claude Code, OpenCode, Copilot o cualquier cosa que hable MCP).
Archivos simples, cero dependencia. Las notas son markdown con frontmatter YAML -- legibles en Obsidian, Foam, Logseq o cualquier editor. La base de datos SQLite es un índice, no la fuente de verdad. Bórrala y reconstruye desde los archivos cuando quieras.
19 herramientas MCP para notas, enlaces, búsqueda, análisis de grafos y gestión de clústeres
6 indicaciones de flujo de trabajo (más las habilidades correspondientes) que codifican el método Zettelkasten para que no lo reaprendas en cada sesión
Búsqueda de texto completo BM25 en títulos y contenido mediante SQLite FTS5
Detección de clústeres que detecta grupos de temas emergentes y genera notas de estructura
Siete tipos de enlaces (referencia, extiende, refina, contradice, cuestiona, apoya, relacionado)
Python 3.10+ | macOS o Linux

Recorrido
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Related MCP server: vault-master-mcp
Inicio rápido
1. Instalación
pipx install slipbox-mcp
# or, with uv:
uv tool install slipbox-mcpEsto coloca un lanzador slipbox-mcp en tu PATH (en ~/.local/bin). Ese único comando es todo el servidor MCP: sin clonar, sin PYTHONPATH, sin ruta de Python de venv fija. Todo lo que sigue lo utiliza. Para probarlo sin instalar nada, uvx slipbox-mcp ejecuta el servidor en un entorno desechable.
(¿Trabajas en el propio Slipbox? Consulta Desarrollo para la configuración de clonado e instalación editable.)
2. Elige un directorio de datos
Una variable, SLIPBOX_BASE_DIR, lo configura todo: las notas se guardan en <base>/data/notes y el índice SQLite en <base>/data/db/zettelkasten.db. El servidor los crea en la primera ejecución con permisos solo para el propietario (0700).
Apunta SLIPBOX_BASE_DIR (o las rutas individuales SLIPBOX_NOTES_DIR / SLIPBOX_DATABASE_PATH que aparecen abajo) a un directorio de datos dedicado que controles, no a una ubicación compartida o del sistema. Estas rutas se usan tal cual: el servidor gestiona el árbol de notas y el índice dentro de ellas, y trata el directorio de notas como la fuente de verdad cuando reconstruye el índice.
# Example: use any absolute path you like
/Users/yourname/.local/share/mcp/slipboxUsa una ruta absoluta completa. Una
~inicial no se expande dentro de los archivos de configuración del cliente MCP y crearía un directorio~literal.
3. Conéctalo a tu cliente MCP
Claude Code (un comando, sin editar archivos):
claude mcp add slipbox \
--env SLIPBOX_BASE_DIR=/Users/yourname/.local/share/mcp/slipbox \
-- slipbox-mcpClaude Desktop (edita el archivo de configuración):
macOS:
~/Library/Application Support/Claude/claude_desktop_config.jsonLinux:
~/.config/claude/claude_desktop_config.json
{
"mcpServers": {
"slipbox": {
"command": "slipbox-mcp",
"env": {
"SLIPBOX_BASE_DIR": "/Users/yourname/.local/share/mcp/slipbox"
}
}
}
}Advertencia sobre el PATH en Desktop: la aplicación de escritorio de macOS no siempre hereda
~/.local/binen su PATH, por lo que el simple"slipbox-mcp"puede no resolverse. Si el servidor no se inicia, reemplaza"command": "slipbox-mcp"por la ruta absoluta que imprimewhich slipbox-mcp(normalmente/Users/yourname/.local/bin/slipbox-mcp).
Otros clientes MCP: registra slipbox-mcp como comando del servidor con SLIPBOX_BASE_DIR en su entorno. El comando y el entorno son los mismos en todas partes.
En lugar de SLIPBOX_BASE_DIR, establece rutas absolutas individualmente. El SLIPBOX_LOG_LEVEL opcional es uno de DEBUG, INFO, WARNING, ERROR.
"env": {
"SLIPBOX_NOTES_DIR": "/Users/yourname/.local/share/mcp/slipbox/notes",
"SLIPBOX_DATABASE_PATH": "/Users/yourname/.local/share/mcp/slipbox/data/db/zettelkasten.db",
"SLIPBOX_LOG_LEVEL": "INFO"
}4. Reinicia y verifica
Reinicia tu cliente (Claude Code se recarga en el próximo lanzamiento; cierra y vuelve a abrir Claude Desktop).
Pregúntale a tu agente:
"Crea una nota de prueba sobre algo"
"Busca test en mi slipbox"
"Encuentra notas huérfanas"
En acción
Lo que ves arriba es el bucle central. Esto es el resto de lo que hace el agente.
Mantenimiento proactivo
El agente lee el recurso slipbox://maintenance-status al inicio de la sesión y muestra los clústeres que necesitan organización.

Búsqueda de texto completo
Búsqueda clasificada por BM25 en todas las notas mediante slipbox_search_notes.

Grafo de conocimiento: notas centrales
slipbox_find_central_notes revela los anclajes estructurales del grafo -- las notas alrededor de las cuales orbita todo lo demás.

Análisis de notas
La indicación analyze_note evalúa la atomicidad, encuentra conexiones reales en el grafo existente, sugiere etiquetas y reescribe para mayor claridad.

Descomposición de fuentes
La indicación knowledge_creation divide un artículo en notas de literatura atómicas con la cita y los enlaces adecuados.

Detección de clústeres
slipbox_get_cluster_report encuentra grupos de etiquetas que coaparecen y que carecen de una nota de estructura. Se puntúan por tamaño, proporción de huérfanas, densidad de enlaces y actualidad.

Creación de notas de estructura
slipbox_create_structure_from_cluster crea el andamiaje de una nota de estructura, enlaza todas las notas miembros y descarta el clúster.

Notas huérfanas
slipbox_find_orphaned_notes saca a la luz conocimiento no integrado -- candidatas para conectar o eliminar.

Notas similares
slipbox_find_similar_notes calcula la similitud a partir de etiquetas compartidas, enlaces comunes y solapamiento de contenido.

Recorrido del grafo
slipbox_get_linked_notes muestra los enlaces tipados de una nota central, agrupados por tipo de enlace.

Síntesis de conocimiento
La indicación knowledge_synthesis encuentra puentes entre áreas no conectadas y propone notas de síntesis a partir de tu conocimiento existente.

Cero dependencia: archivos simples en Obsidian
Las notas son markdown simple. Abre la bóveda en Obsidian y todo funciona -- contenido renderizado, enlaces inversos y el grafo de conocimiento.
Para un grafo que represente los enlaces tipados en color (apoya, extiende, refina, ...) en lugar del grafo integrado no tipado de Obsidian, instala el plugin complementario Slipbox Semantic Graph -- una vista de grafo dirigido por fuerzas con títulos legibles y tipos de enlaces semánticos codificados por colores. Instálalo manualmente desde el lanzamiento 0.1.0: copia main.js, manifest.json y styles.css en <vault>/.obsidian/plugins/slipbox-graph/ y luego actívalo en Configuración → Plugins de la comunidad. (Una vez que se acepte en el directorio oficial, también podrás instalarlo mediante Configuración → Plugins de la comunidad → Explorar → buscar "Slipbox Semantic Graph".) Lee la misma sección id del frontmatter y ## Links que escribe el servidor, por lo que no se necesita configuración adicional. Abre la vista con el comando Abrir grafo semántico (Paleta de comandos) o con el icono de la cinta git-fork.

La leyenda de la parte superior asigna cada color a un tipo de enlace (extiende, refina, apoya, contradice, cuestiona, relacionado). Enfoca una nota de estructura y su constelación aparece. Aquí, Contract Testing Knowledge Map con sus notas miembros orbitando a su alrededor:

Opcional: detección automática de clústeres
El análisis de clústeres escanea todas las notas y calcula puntuaciones de similitud. Ejecutarlo a diario (6 a. m.) precalcula los resultados para que slipbox_get_cluster_report() responda al instante. Sin programación, la detección de clústeres se ejecuta bajo demanda, lo que resulta más lento para colecciones grandes.
Ejecútalo manualmente después de importaciones masivas, reorganizaciones importantes o cuando quieras resultados inmediatos.
Instalar la detección de clústeres (macOS)
chmod +x scripts/install-cluster-detection.sh
./scripts/install-cluster-detection.shEl instalador detecta tu ruta de Python/venv, genera el plist de LaunchAgent y lo carga.
Prueba manual (vigilante de archivos)
source .venv/bin/activate
python scripts/detect_clusters.pyLa salida se guarda en ~/.local/share/mcp/slipbox/cluster-analysis.json.
Desinstalar la detección de clústeres
./scripts/install-cluster-detection.sh --uninstallOpcional: vigilante de archivos de macOS para la indexación automática
El servidor MCP mantiene un índice de base de datos para búsquedas rápidas. Editar notas en Obsidian (o en cualquier editor) deja la base de datos desactualizada hasta que ejecutes slipbox_rebuild_index.
El vigilante de archivos se ejecuta como demonio en segundo plano, supervisa tu directorio de notas y reconstruye automáticamente el índice cuando cambian los archivos .md.
Úsalo si editas notas con frecuencia en Obsidian mientras usas Claude.
Instalar el vigilante de archivos (macOS)
chmod +x scripts/install-file-watcher.sh
./scripts/install-file-watcher.shEl instalador detecta tu ruta de Python/venv, instala watchdog si es necesario y carga el LaunchAgent. Se inicia al iniciar sesión y se reinicia si falla.
Prueba manual
source .venv/bin/activate
python scripts/watch_notes.pyEdita un archivo de nota. Deberías ver "reconstruyendo índice..." en la salida del vigilante.
Comprobar el estado
launchctl list | grep slipbox.watcher
# View logs
tail -f ~/.local/share/mcp/slipbox/watcher.logDesinstalar el vigilante de archivos
./scripts/install-file-watcher.sh --uninstallIndicación del sistema recomendada
Slipbox incluye una línea base automáticamente: cada cliente recibe las instrucciones del servidor al conectarse, que cubren cómo usar bien las herramientas -- tipos de notas, semántica de enlaces, estándares de calidad y flujos de trabajo básicos como buscar antes de crear. No tienes que añadirlas tú.
docs/SYSTEM_PROMPT.md es la capa de activación voluntaria que se añade encima: las directivas de autonomía e iniciativa que un servidor no debería imponer por su cuenta. Añádela a las preferencias de tu agente o a tu indicación del sistema para activar:
Captura automática de conocimiento durante las conversaciones
Detección de clústeres emergentes al inicio de la conversación
Referencia de herramientas
Operaciones básicas con notas
Tool | Descripción |
| Crear notas atómicas (fugaces/de literatura/permanentes/de estructura/hub) |
| Obtener una nota por ID o título |
| Actualizar notas existentes |
| Eliminar notas |
Enlaces
Tool | Descripción |
| Crear enlaces semánticos entre notas |
| Eliminar enlaces |
| Eliminar un enlace específico (da error si el enlace no existe) |
| Obtener notas enlazadas hacia/desde una nota |
Búsqueda y descubrimiento
Herramienta | Descripción |
| Buscar por texto (clasificado por BM25), etiquetas o tipo |
| Encontrar notas similares a una nota dada |
| Encontrar las notas más conectadas |
| Encontrar notas no conectadas |
| Listar notas por rango de fechas |
| Listar todas las etiquetas |
Análisis de clústeres
Herramienta | Descripción |
| Obtener clústeres pendientes que necesitan notas de estructura |
| Crear nota de estructura a partir del clúster |
| Regenerar el análisis de clústeres |
| Descartar permanentemente el clúster de las sugerencias |
Mantenimiento
Herramienta | Descripción |
| Reconstruir el índice de la base de datos a partir de archivos |
Referencia de prompts
Los prompts MCP son plantillas de flujo de trabajo reutilizables que codifican el método Zettelkasten para que no tengas que volver a explicarlo en cada sesión.
Prompt | Descripción | Cuándo usarlo |
| Procesar información en 3-5 notas atómicas | Al añadir artículos, ideas o notas |
| Procesar volúmenes más grandes en 5-10 notas | Al procesar libros o contenido extenso |
| Mapear conexiones con el conocimiento existente | Al explorar cómo se relacionan los temas |
| Crear ideas de orden superior | Al encontrar puentes entre ideas |
| Evaluar la idoneidad de una nota para el slipbox | Al revisar una nota nueva o existente |
| Mostrar tareas de mantenimiento pendientes | Al inicio de una sesión de trabajo |
Cómo invocar: comandos slash y skills
Cada flujo de trabajo incluye dos formas:
Prompts MCP: servidos por el servidor en ejecución.
Skills: paquetes independientes (
skills/<name>/) que ejecutan el mismo flujo de trabajo y añaden activación por lenguaje natural.
Cinco de las seis skills se generan a partir de las mismas plantillas PROMPT_* que usa el servidor (src/slipbox_mcp/server/descriptions.py), y el CI falla si las skills/ confirmadas se desvían de esas plantillas. La sexta, cluster-maintenance, se crea directamente en scripts/build_skills.py porque su prompt MCP es un mensaje de estado renderizado en tiempo de ejecución en lugar de un flujo de trabajo reutilizable.
Los comandos slash son la vía fiable. Claude Code muestra los prompts MCP como /mcp__<server>__<prompt>; escribe /mcp__slipbox-mcp__ para abrir el selector:
/mcp__slipbox-mcp__knowledge_creation
/mcp__slipbox-mcp__knowledge_exploration
/mcp__slipbox-mcp__knowledge_synthesis
/mcp__slipbox-mcp__knowledge_creation_batch
/mcp__slipbox-mcp__analyze_note
/mcp__slipbox-mcp__cluster_maintenance(Las skills instaladas también exponen sus propios comandos slash por nombre de directorio, p. ej. /slipbox-analyze-note).
El lenguaje natural funciona una vez instalada la skill correspondiente. Solo describe lo que quieres:
Analyze this note for my slipbox: [paste note]
Add this to my slipbox: [paste article]
Synthesize my notes on attention and memory.La activación por prosa depende de que la skill esté instalada y de que tu redacción coincida con su descripción; recurre al comando slash si no se activa. Pedir al modelo que «use el prompt analyze_note» por su nombre no funciona. El modelo no puede invocar un prompt MCP por nombre. Usa un comando slash o deja que una skill se active mediante lenguaje natural.
Instalación de skills
Claude Code descubre las skills desde .claude/skills/ (por proyecto) o ~/.claude/skills/ (global), no desde un skills/ de nivel superior sin más. Crea enlaces simbólicos o copia las que quieras en una ruta de descubrimiento (p. ej., para este proyecto):
mkdir -p .claude/skills
ln -s ../../skills/slipbox-analyze-note .claude/skills/slipbox-analyze-note
# ...or copy the directories, or symlink all sixClaude Desktop necesita cada skill como un paquete .skill. Constrúyelos y luego súbelos:
python scripts/build_skills.py # writes dist/*.skillVe a Configuración → Skills → Upload skill y selecciona los paquetes de dist/ que quieras. Cada uno se instala como comando slash y como activador de lenguaje natural.
Después de editar una plantilla de prompt en descriptions.py, vuelve a ejecutar la compilación para regenerar las skills.
Tipos de enlaces
Tipo | Cuándo usarlo | Inverso |
| Conexión genérica «ver también» | reference |
| Construir sobre otra idea | extended_by |
| Aclarar o mejorar | refined_by |
| Punto de vista opuesto | contradicted_by |
| Plantear preguntas sobre | questioned_by |
| Proporcionar evidencia para | supported_by |
| Conexión temática laxa | related |
Tipos de notas
Tipo | Propósito |
| Capturas rápidas, pensamientos sin procesar |
| Ideas de fuentes con cita |
| Ideas refinadas con tus propias palabras |
| Mapas que organizan 7-15 notas relacionadas sobre un tema específico |
| Visión general del dominio que enlaza con notas de estructura; punto de entrada para navegar por un área amplia de conocimiento |
Estructura vs. Hub: Una nota de estructura organiza un clúster de notas permanentes en torno a un único tema. Es un mapa curado un nivel por encima de las propias notas. Una nota hub opera un nivel aún más alto: enlaza con notas de estructura (y ocasionalmente con notas permanentes clave) en todo un dominio de conocimiento. Mientras que una nota de estructura responde a «¿qué sé sobre X?», una nota hub responde a «¿cómo está organizado mi conocimiento de todo este dominio?». La mayoría de los Zettelkasten solo necesitan unas pocas notas hub.
Formato de archivo
Las notas se almacenan como archivos Markdown con frontmatter YAML:
---
id: "20251217T172432480464000"
title: "Poetry Revision Principles"
type: structure
tags:
- poetry
- revision
- craft
created: "2025-12-17T17:24:32"
updated: "2025-12-17T17:24:32"
---
# Poetry Revision Principles
Content here...
## Links
- reference [[20250728T125429845760000]] Member of structurePuedes editar estos archivos directamente en cualquier editor de texto u Obsidian. Ejecuta slipbox_rebuild_index después de ediciones externas.
Actualización
Después de obtener nuevas versiones, reinicia Claude Desktop. Si las notas de la versión mencionan cambios en la base de datos, ejecuta slipbox_rebuild_index una vez para poner tu base de datos existente al día.
Actualización a la búsqueda FTS5 (cualquier versión posterior al lanzamiento de FTS5): El índice de búsqueda de texto completo se crea automáticamente cuando el servidor se inicia con una base de datos nueva. Para bases de datos existentes, la tabla FTS5 se creará en el primer arranque, pero estará vacía hasta que ejecutes:
slipbox_rebuild_indexEsto rellena el índice BM25 a partir de tus notas existentes. Los resultados de búsqueda no se clasificarán por relevancia hasta que se haga esto.
Solución de problemas
El servidor no se carga en Claude Desktop
Confirma que el lanzador se resuelve:
which slipbox-mcpdebería imprimir una ruta (normalmente~/.local/bin/slipbox-mcp).Si se resuelve en tu terminal pero Desktop aún no puede iniciarlo, la aplicación GUI no está viendo
~/.local/binen su PATH. Reemplaza"command": "slipbox-mcp"con la ruta absoluta del paso 1.Revisa los registros de Claude Desktop para ver errores.
slipbox-mcp: command not found
El script de consola no se instaló o no está en el PATH. Reinstala con pipx install --editable . --force y luego verifica con which slipbox-mcp. Si el directorio bin de pipx falta en el PATH, ejecuta pipx ensurepath y reinicia tu shell.
El directorio de notas apunta a ~/... literalmente
Si tu directorio de notas termina en ./~/... relativo al directorio de trabajo actual (CWD), usaste ~ en la configuración JSON. Claude Desktop no expande ~. Reemplázalo con la ruta absoluta completa.
La búsqueda no devuelve resultados
El índice FTS5 puede no estar poblado. Ejecuta
slipbox_rebuild_indexuna vez para indexar las notas existentes.Si editaste notas recientemente fuera de Claude, el índice puede estar desactualizado. Ejecuta
slipbox_rebuild_index.
slipbox_list_notes_by_date devuelve resultados vacíos
Si start_date es posterior a end_date, no coincide ninguna nota y se devuelve un resultado vacío. Este es el comportamiento esperado, no un error.
Base de datos desincronizada
Si las notas se editaron fuera del servidor MCP:
slipbox_rebuild_indexLa detección de clústeres no se está ejecutando
launchctl list | grep slipbox.cluster-detection
# Should show: - 0 com.slipbox.cluster-detection
# Check logs
cat /tmp/slipbox-clusters.log
# Reinstall if needed
./scripts/install-cluster-detection.sh --uninstall
./scripts/install-cluster-detection.shEl observador de archivos no se está ejecutando
launchctl list | grep slipbox.watcher
# Should show: - 0 com.slipbox.watcher
# Check logs
cat ~/.local/share/mcp/slipbox/watcher.log
# Reinstall if needed
./scripts/install-file-watcher.sh --uninstall
./scripts/install-file-watcher.shActualización desde variables de entorno ZETTELKASTEN_*
Si antes usabas ZETTELKASTEN_NOTES_DIR, ZETTELKASTEN_DATABASE_PATH u otras variables ZETTELKASTEN_*, ya no se leen. Renómbralas a sus equivalentes SLIPBOX_*:
Antiguo | Nuevo |
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El servidor registra una advertencia si detecta los nombres antiguos, pero no los migra automáticamente.
La ruta del informe de clústeres no es configurable
El informe de análisis de clústeres siempre se escribe en ~/.local/share/mcp/slipbox/cluster-analysis.json, independientemente de SLIPBOX_BASE_DIR o SLIPBOX_NOTES_DIR. Si usas rutas no predeterminadas, el informe de clústeres seguirá estando en la ubicación predeterminada.
Los scripts de instalación son solo para macOS
Los scripts scripts/install-cluster-detection.sh y scripts/install-file-watcher.sh usan launchctl y ~/Library/LaunchAgents/, que solo existen en macOS. En Linux, tendrás que crear unidades systemd o trabajos cron equivalentes manualmente. Consulta los comandos de prueba manuales en las secciones correspondientes del README para verificar que los scripts Python subyacentes funcionan en tu plataforma.
Las rutas predeterminadas son relativas al directorio de trabajo
Si SLIPBOX_NOTES_DIR y SLIPBOX_DATABASE_PATH no están configurados, el servidor usa por defecto data/notes y data/db/zettelkasten.db relativos al directorio de trabajo actual. Cuando se ejecuta mediante Claude Desktop, el CWD puede no ser el que esperas. Configura siempre rutas absolutas en claude_desktop_config.json para evitarlo.
Desarrollo
Configuración
git clone https://github.com/jamesfishwick/slipbox-mcp.git
cd slipbox-mcp
uv venv && uv pip install -e ".[dev]"Pruebas
El proyecto tiene tres niveles de pruebas:
Nivel | Número | Velocidad | Coste | Comando |
Unitarias + integración | 219 | ~2s | Gratis |
|
Pruebas de contrato de herramientas | 22 | ~0.5s | Gratis |
|
Evaluaciones LLM | 28 | ~10min | ~3-5 $ |
|
# Default: runs unit + contract tests (CI runs this)
pytest
# Run everything except LLM evals
pytest tests/ evals/tool_contracts/
# Run LLM evals (requires claude CLI authenticated)
pytest evals/llm/ -v
# Run LLM evals with a specific model
EVAL_MODEL=sonnet pytest evals/llm/ -v
# Lint
ruff check src/ evals/Las pruebas unitarias cubren la lógica interna: servicios, repositorio, modelos, análisis sintáctico.
Las pruebas de contrato de herramientas verifican el formato de salida de las herramientas MCP que ve el LLM: estructura analizable, encadenamiento (create -> search -> get) y mensajes de error útiles. Son deterministas y no llaman a ningún LLM.
Las evaluaciones LLM envían prompts a un LLM mediante la CLI claude con el servidor MCP conectado y luego califican los resultados inspeccionando el estado de la base de datos (notas creadas, enlaces hechos, etiquetas aplicadas). Prueban si el LLM usa realmente las herramientas correctamente dadas las descripciones de las herramientas.
CI/CD
Protección de ramas: Los envíos directos a main están bloqueados. Todos los cambios pasan por PR.
Workflow | Disparador | Runner | Qué |
| Cada PR + push a main | Alojado en GitHub | Pruebas unitarias y de contrato, ruff lint + format |
| Opt-in (etiqueta o manual) | Autoalojado | 28 evaluaciones de LLM mediante claude CLI |
| Push a | Alojado en GitHub | PR de release-please; al fusionarse, compilar y publicar en PyPI |
La suite de evaluación de LLM es costosa (~$3-5, ~10 min) y se ejecuta en un runner autoalojado, por lo que nunca se ejecuta automáticamente. Un disparador basado en rutas no puede distinguir un cambio real de prompt de un reformateo cosmético. Ejecútala deliberadamente cuando cambies la semántica de un prompt o de una descripción de herramienta:
Añade la etiqueta
run-llm-evalsal PR. Se ejecuta y se vuelve a ejecutar en cada push mientras la etiqueta esté presente.O actívala manualmente desde la pestaña Actions (
workflow_dispatch).O ejecútala localmente sin el runner:
pytest evals/llm/ -v.
Sin etiqueta ni ejecución manual, el trabajo se omite (no se asigna runner, sin coste).
Personalización de la configuración de evaluación
Si no quieres un runner autoalojado: elimina .github/workflows/llm-evals.yml y ejecuta pytest evals/llm/ -v localmente antes de fusionar cambios de prompt.
Si quieres evaluaciones de LLM en cada PR automáticamente: añade un disparador pull_request con el filtro paths: relevante y elimina la condición de etiqueta en el if: del trabajo. Pero espera disparadores incidentales por ediciones solo de formato.
Para cambiar el modelo de evaluación predeterminado: establece EVAL_MODEL en tu entorno o en el archivo de workflow. El valor predeterminado es haiku por velocidad/coste.
Para configurar un runner autoalojado:
# Get a registration token
gh api repos/OWNER/REPO/actions/runners/registration-token -X POST -q '.token'
# Download and configure
mkdir -p ~/.github-runners/slipbox-mcp && cd ~/.github-runners/slipbox-mcp
curl -sL -o actions-runner.tar.gz https://github.com/actions/runner/releases/latest/download/actions-runner-osx-arm64-2.325.0.tar.gz
tar xzf actions-runner.tar.gz
./config.sh --url https://github.com/OWNER/REPO --token <TOKEN> --unattended
nohup ./run.sh &Publicación en PyPI
Las releases están automatizadas. El workflow Release (.github/workflows/release.yml) ejecuta release-please en cada push a main y publica mediante Trusted Publishing de PyPI (OIDC, por lo que no se almacena ningún token de API en los secretos del repositorio).
El flujo (nunca editas una versión a mano ni haces push de una etiqueta):
Lleva los cambios a
maincon mensajes de Conventional Commit (feat:→ incremento menor,fix:→ parche,feat!:/BREAKING CHANGE:→ mayor). Los hooks de commit del repositorio ya imponen esta forma.release-please mantiene abierto de forma permanente un "release PR", acumulando el próximo incremento de versión (en
src/slipbox_mcp/__init__.py) y las entradas deCHANGELOG.mdderivadas de esos commits.Cuando estés listo para publicar, fusiona el release PR. Eso etiqueta la release (
v<version>) y, en la misma ejecución del workflow, compila el sdist + wheel, ejecutatwine checky publica en PyPI.
Así que publicar una release es un clic: fusiona el PR del bot. Nada más.
Los tipos de commit deciden la versión. Así que indica el tipo con precisión. El incremento se calcula mecánicamente a partir de los prefijos de Conventional Commit desde la última release, no del tamaño del cambio. Reserva feat:/fix: para cambios en el paquete publicado; usa los tipos sin release para todo lo demás:
Prefijo | Efecto en la versión | Úsalo para |
| menor (1.3.0 → 1.4.0) | nueva capacidad de ejecución en el paquete |
| parche (1.3.0 → 1.3.1) | corrección de errores en el paquete |
| mayor (1.3.0 → 2.0.0) | cambio incompatible con versiones anteriores |
| ninguno | documentación, herramientas, CI, empaquetado, cambios solo internos |
Un lote de solo commits sin release no produce ningún release PR en absoluto. El título del squash-merge es el commit que lee release-please, así que el prefijo del título del PR es lo que cuenta. Etiquétalo por lo que gana el paquete, no por el esfuerzo invertido.
Configuración única (ya hecha para este repositorio, documentada para forks):
En PyPI, registra un publicador de confianza pendiente para el proyecto
slipbox-mcp: Owner:jamesfishwick· Repository:slipbox-mcp· Workflow:release.yml· Environment:release. Los cuatro deben coincidir exactamente.En GitHub, crea un entorno llamado
release(Settings → Environments). Si restringes sus refs de despliegue, añade una regla de etiquetav*(una regla de rama con el mismo nombre no coincidirá con la etiqueta).
La versión se define una sola vez, en
src/slipbox_mcp/__init__.py(release-please la incrementa; el marcador# x-release-please-versionle indica qué línea).pyproject.toml(dynamic = ["version"]) y elserver_versiondel servidor leen de ahí, así que no hay nada que mantener sincronizado; la etiqueta que release-please crea siempre coincide con la versión del paquete por construcción.
Para ensayar una compilación sin publicar, ejecútala a mano: python -m build && twine check dist/* (y twine upload --repository testpypi dist/* con un token de TestPyPI para simular la subida).
Constantes compartidas de prompts
Todas las descripciones de herramientas y las plantillas de prompts están en src/slipbox_mcp/server/descriptions.py. Tanto el servidor MCP como las pruebas de evaluación importan desde esta única fuente de verdad. Si cambias un prompt, las evaluaciones comprueban si el LLM sigue comportándose correctamente con la nueva redacción.
Registro de depuración
SLIPBOX_LOG_LEVEL=DEBUG python -c "from slipbox_mcp.main import main; main()"Herramienta CLI
El comando slipbox proporciona acceso desde la terminal para operaciones mecánicas:
slipbox status # Overview of notes, tags, orphans, pending clusters
slipbox search <query> # Find notes by text
slipbox clusters # Show pending structure note candidates
slipbox orphans # List unconnected notes
slipbox rebuild # Rebuild index (add --clusters to refresh cluster analysis)
slipbox export <id> # Export note markdown to stdout
slipbox tags # List all tags with usage countsInstalación: pipx install --editable . (añade slipbox a tu PATH)
Experimental: Slipbox como memoria del agente
Una hipótesis no probada, no una configuración recomendada. Todo lo anterior ayuda a un agente a gestionar tu conocimiento. Esto lo invierte: el agente usa un slipbox como su propia memoria persistente entre sesiones, en lugar de la memoria nativa o un archivo de reglas.
El modelo no tiene memoria entre sesiones, así que el slipbox es el único canal que una sesión deja para la siguiente. Escribe informes para un sucesor en frío (un fallo y por qué, una restricción recurrente, una corrección, un dato ganado con esfuerzo), los etiqueta con agent-memory, y busca esa etiqueta antes de actuar. La apuesta es que una memoria conectada supera a un archivo de reglas plano, porque la recuperas mediante recorrido.
Tres cosas que debes saber primero: el aislamiento de espacios de nombres es una convención de etiquetas, no un mecanismo impuesto, así que ejecútalo contra una instancia de slipbox separada; "memoria" es un nombre inapropiado, ya que no persiste nada excepto las propias notas; y la disciplina de crecimiento es la parte no probada, así que espera un crecimiento desordenado en la primera ejecución. Explicación completa y advertencias: Slipbox as Agent Self-Memory.
Documentación
Doc | Qué contiene |
Formato del ID de nota, los cinco tipos de nota y una chuleta de una página para el método. | |
Ejecutar el mismo flujo de trabajo a mano en Obsidian, sin intervención de un agente. | |
Cómo se mapean los enlaces de Slipbox a | |
Qué otras herramientas pueden leer y escribir en el mismo vault. | |
La capa de autonomía opcional: captura automática, detección de clústeres y el experimento de memoria del agente. | |
Una sesión práctica que muestra las herramientas en uso. |
Contribución
Consulta CONTRIBUTING.md para las instrucciones de configuración, los estándares de codificación y cómo enviar cambios.
Hoja de ruta
Consulta ROADMAP.md para las funciones planificadas y la dirección futura.
Patrocinio
Si slipbox-mcp te resulta útil, considera patrocinar el proyecto.
Licencia
MIT
Available Tools
19 toolsslipbox_create_linkA
Create a semantic link between two notes.
Links are directional: source -> target. Use bidirectional=true for important relationships (automatically creates inverse link type).
Link Types:
reference: Generic "see also" connection
extends: Source builds upon target (inverse: extended_by)
refines: Source clarifies or improves target (inverse: refined_by)
contradicts: Source presents opposing view (inverse: contradicted_by)
questions: Source raises questions about target (inverse: questioned_by)
supports: Source provides evidence for target (inverse: supported_by)
related: Loose thematic connection (symmetric)
Best Practices:
Always add description explaining WHY notes are linked
Use bidirectional=true for substantive relationships
Create links immediately after creating notes
Args: source_id: ID of the source note (the note doing the linking) target_id: ID of the target note (the note being linked to) link_type: One of reference/extends/refines/contradicts/questions/supports/related description: Brief explanation of the relationship bidirectional: If true, creates inverse link from target to source
| Name | Required | Description | Default |
|---|---|---|---|
| link_type | No | reference | |
| source_id | Yes | ||
| target_id | Yes | ||
| description | No | ||
| bidirectional | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It explains directionality (source -> target), the automatic inverse link when bidirectional=true, and enumerates all link types with their inverses. This covers core behavior well, though it omits details about error handling or note existence validation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with sections for purpose, link types, best practices, and args. It is fairly long but every sentence is informative and avoids redundancy, making it easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
It covers all essential details for correct invocation: direction, link types, bidirectional behavior, parameter meanings, and best practices. The existence of an output schema means return values need not be explained. It lacks edge-case details (e.g., duplicate links), but that is not critical.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0% (schema only gives types and defaults), but the description fully compensates by explaining each parameter, including the valid values for link_type and the meaning of bidirectional. This adds significant semantic value beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: creating a semantic link between two notes. It distinguishes itself from sibling tools like slipbox_delete_link and slipbox_get_linked_notes by focusing on the create operation, and it adds nuance with directionality and link type semantics.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It provides best practices (e.g., 'Create links immediately after creating notes') and explains when to use bidirectional=true, giving clear contextual guidance. However, it does not explicitly compare to alternatives like slipbox_remove_link or slipbox_get_linked_notes, which is a minor gap.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
slipbox_create_noteA
Create a new atomic Zettelkasten note.
Each note should contain exactly one idea. After creating, immediately
link to related notes using slipbox_create_link.
Note Types:
- fleeting: Quick captures, unprocessed thoughts (process within 24-48 hours)
- literature: Ideas extracted from sources. REQUIRES at least one entry
in references (citation or URL). If you don't yet have the citation,
use 'fleeting' as a staging type and promote to 'literature' once
the source is attached.
- permanent: Refined ideas in the user's own words (the core of your Zettelkasten)
- structure: Maps organizing 7-15 related notes on a topic
- hub: Entry points into major knowledge domains
Authoring:
- Store `content` exactly as the user provided it. Do NOT rewrite,
expand, or restructure their words. Compose or refine content only
when explicitly asked (e.g. "draft", "refine", "make it standalone").
Best Practices:
- Title should express the idea in brief (understandable without reading content)
- A refined permanent note typically runs 3-7 paragraphs and stands
alone; match the length of what the user gave you, though — a
one-line capture stays one line.
- Use 2-5 specific tags; prefer existing tags when they fit
- Search first (slipbox_search_notes) to avoid duplicating existing notes
Args:
title: Concise title expressing the core idea
content: Full note content in markdown
note_type: One of fleeting/literature/permanent/structure/hub (default: permanent)
tags: Comma-separated tags, e.g. "poetry,revision,craft"
references: Newline-separated citations to external sources (e.g. "Ahrens, S. (2017). How to Take Smart Notes.
| Name | Required | Description | Default |
|---|---|---|---|
| tags | No | ||
| title | Yes | ||
| content | Yes | ||
| note_type | No | permanent | |
| references | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full behavioral burden and excels: it mandates exact content preservation ('Store content exactly as the user provided it'), explains note type behavior, requires references for literature notes, and warns against rewriting unless explicitly asked. This goes far beyond what the schema alone conveys.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is long but appropriately structured into labeled sections (Note Types, Authoring, Best Practices, Args), with every section adding practical guidance. The core purpose is front-loaded in the first sentence, and the Args section maps cleanly to the schema.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a complex creation tool with no annotations, the description covers note type semantics, staging workflow, content fidelity, tagging, duplicate avoidance, linking, and parameter formats. An agent has enough context to invoke it correctly without needing return-value details, especially since an output schema exists.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description is the only source of parameter meaning. It defines title, content, note_type, tags, and references with formats, examples, defaults, and type-specific constraints, fully compensating for the empty schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with 'Create a new atomic Zettelkasten note,' a specific verb, resource, and core constraint. It clearly differentiates itself from siblings like slipbox_create_link and slipbox_update_note by focusing on note creation and atomicity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives explicit guidance: search first with slipbox_search_notes to avoid duplicates, create links immediately after with slipbox_create_link, and use 'fleeting' as a staging type when citations are missing. These are concrete when/when-not and alternative-tool instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
slipbox_create_structure_from_clusterA
Create a structure note from a detected cluster.
Generates a structure note organizing all notes in the cluster, with bidirectional links to each member note.
Run slipbox_get_cluster_report first to see available clusters and their IDs.
Args: cluster_id: ID from cluster report (e.g. "jackson-mac-low-chance-operations") title: Override the suggested title (optional) create_links: Create bidirectional links to member notes (default: true)
| Name | Required | Description | Default |
|---|---|---|---|
| title | No | ||
| cluster_id | Yes | ||
| create_links | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries the behavioral burden. It discloses that the tool generates a structure note, organizes all member notes, and creates bidirectional links, and it notes the default create_links behavior in Args. It does not discuss failure modes or side effects on the cluster state, but for a creation operation the core behavior is transparent enough.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The purpose is front-loaded, the prerequisite is in a short sentence, and the Args section is scannable. There is minor redundancy between the opening sentence and 'Generates a structure note...', but the added detail about bidirectional links earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With an output schema present, the agent does not need the description to explain return values. The description covers the prerequisite, the behavior, and all parameters, which is sufficient for a 3-parameter creation tool with no nested objects. It does not address potential errors like invalid cluster IDs, but that is a minor gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must explain all parameters. It does: cluster_id is defined as an ID from the cluster report with a concrete example, title is an optional override of the suggested title, and create_links is explained with its default true. This fully compensates for the empty schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb and resource: 'Create a structure note from a detected cluster.' It further clarifies that it organizes all notes in the cluster and creates bidirectional links, which distinguishes it from generic slipbox_create_note and from cluster-management siblings like slipbox_dismiss_cluster.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It gives a concrete prerequisite: 'Run slipbox_get_cluster_report first to see available clusters and their IDs.' This tells an agent when in the workflow to call the tool. It does not explicitly state when not to use it or name alternatives, so it falls short of full routing guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
slipbox_delete_linkA
Delete a specific link from one note to another.
Unlike slipbox_remove_link, this tool returns an error if no link exists between the two notes.
Args: source_id: ID of the source note (the note containing the link) target_id: ID of the target note (the note being linked to)
| Name | Required | Description | Default |
|---|---|---|---|
| source_id | Yes | ||
| target_id | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of disclosing behavior. It clearly conveys deletion and adds a meaningful edge-case behavior: it errors when no link exists. It does not discuss side effects, permissions, or reversibility, but for a targeted link deletion this is reasonably transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and front-loaded, with the primary action first, the differentiating behavior second, and parameter explanations last. Every sentence earns its place with no filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a two-parameter tool, the description covers the action, the key error condition, both parameter roles, and the distinction from the closest sibling. An output schema exists, so return-value details do not need to be spelled out here.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description fully compensates by explaining both parameters: source_id is the note containing the link, and target_id is the note being linked to. This adds real meaning beyond the bare schema property names.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb and resource: 'Delete a specific link from one note to another.' It also distinguishes itself from the sibling slipbox_remove_link by noting its error behavior, so an agent can tell the two apart without inspecting schemas.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly names the sibling slipbox_remove_link and explains the key difference: this tool returns an error if no link exists. This makes the choice between the two tools clear, though it stops short of stating an explicit 'use this when you need...' rule.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
slipbox_delete_noteA
Delete a note permanently.
Warning: This also removes all links to and from this note. Consider updating note_type to "fleeting" instead if uncertain.
Args: note_id: The ID of the note to delete
| Name | Required | Description | Default |
|---|---|---|---|
| note_id | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full disclosure burden. It warns that the deletion is permanent and that 'This also removes all links to and from this note', which are the critical side effects an agent must know before invoking the tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and front-loaded: the purpose and the destructive warning appear immediately, and the Args section is minimal without unnecessary padding. Every sentence serves a purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a destructive tool with no annotations, the description covers the essential facts: irreversible deletion, link side effects, and a safer alternative. An output schema exists, so return-value documentation is not required, and nothing necessary for correct invocation is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate for the single parameter. However, it only restates 'The ID of the note to delete', which adds little beyond the parameter name 'note_id'. It omits any guidance on the ID format, where to obtain it, or whether any validation constraints apply.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Delete a note permanently' – a specific verb, resource, and degree of effect. It distinguishes this from slipbox_delete_link and slipbox_update_note, and the context makes the destructive scope unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly advises 'Consider updating note_type to "fleeting" instead if uncertain', naming a concrete alternative for the uncertain case. This gives clear when-to-use and when-not-to-use guidance, with no ambiguity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
slipbox_dismiss_clusterA
Permanently dismiss a cluster from maintenance suggestions.
Use this when a cluster has been reviewed and determined not to need a structure note, or when the user doesn't want to be reminded about it.
Args: cluster_id: The cluster ID to dismiss (e.g. "poetry-craft-revision")
| Name | Required | Description | Default |
|---|---|---|---|
| cluster_id | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of disclosing safety and effects. It clearly states the action is 'permanently' applied and scoped to maintenance suggestions, which indicates irreversibility. It could go further by clarifying that no notes are deleted, but the permanence and scope are sufficiently conveyed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and front-loaded: it states the core purpose in the first sentence, gives a short usage rationale, and includes a minimal Args section with an example. No sentence is wasted.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple with one required parameter and an output schema present, so return-value details are unnecessary. The description covers purpose, usage timing, permanence, and parameter example. A small gap is that it does not tell the user how to discover cluster IDs, but that is likely covered by sibling tools like get_cluster_report.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage for cluster_id, so the description must compensate. It adds a concrete example ('poetry-craft-revision') and identifies the parameter as a cluster ID, but beyond that it mostly restates the parameter name. For a single simple string parameter, this is adequate but not enriching.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('dismiss') with a clear resource ('cluster') and scope ('from maintenance suggestions'). It clearly distinguishes this from sibling tools like create_structure_from_cluster by positioning it as the 'don't remind me' action.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use the tool: after a cluster has been reviewed and determined not to need a structure note, or when the user doesn't want reminders. It provides clear context but does not name or exclude specific alternative tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
slipbox_find_central_notesA
Find the most connected notes in the Zettelkasten.
Central notes have the most incoming and outgoing links, making them key hubs in your knowledge network. Good candidates for hub notes.
Args: limit: Maximum results (default: 10)
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the behavioral disclosure burden. It conveys a non-mutating query ('Find') and explains the central-note concept via incoming/outgoing links, but it does not state how results are ordered, how ties are handled, or whether 'limit' truncates a fully computed ranking. These are moderate gaps for a no-annotation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose and keeps explanatory text to two useful sentences before the Args block. It is compact and readable, though the Args section mostly duplicates the schema's default value rather than adding new information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a one-optional-parameter tool with an output schema and no annotations, the description covers the essential purpose, the definition of central notes, and the limit parameter. It does not detail sorting or algorithm internals, but those are unlikely to be required for correct invocation; the main missing piece is explicit sibling-tool routing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has only one parameter with 0% description coverage, and the description adds 'Maximum results (default: 10)', which clarifies that limit means result count and matches the schema default. It provides no guidance on allowed ranges, zero/negative values, or truncation behavior, but the parameter is simple enough that this is only a minor gap.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with 'Find the most connected notes in the Zettelkasten', a specific verb+resource statement, then defines central notes as hubs with the most incoming and outgoing links. This clearly distinguishes the tool from siblings like find_orphaned_notes or find_similar_notes by focusing on graph centrality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
'Good candidates for hub notes' implies the intended use case of identifying key knowledge hubs. However, it never explicitly tells an agent when to prefer this tool over the many sibling search/find tools, nor when not to use it, so the guidance is implied rather than fully actionable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
slipbox_find_orphaned_notesA
Find notes with no connections to other notes.
Orphaned notes represent unintegrated knowledge. Review these periodically to either link them to existing notes or identify candidates for deletion.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden of disclosing behavior. It correctly implies a read-only operation via the verb 'Find' and adds the interpretive context that orphaned notes represent unintegrated knowledge. However, it does not explicitly state side-effect freedom, output behavior, or other operational traits beyond the core result.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, front-loads the primary function in the first sentence, and each subsequent sentence adds meaningful context about why orphaned notes matter and what to do with the results. No filler or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter, read-only discovery tool with an output schema available, the description covers what the tool returns, why it matters, and how to act on the results. Nothing essential for invoking it correctly is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters and the schema coverage is 100%, so parameter documentation is unnecessary. Baseline for 0 params is 4; the description adds no parameter details but none are needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb and resource: 'Find notes with no connections to other notes.' This clearly distinguishes the tool from siblings like slipbox_find_similar_notes and slipbox_find_central_notes, because it targets a unique criterion: absence of connections.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use the tool: 'Review these periodically to either link them to existing notes or identify candidates for deletion.' It does not explicitly name alternative tools or state when not to use it, but the periodic-review purpose is useful and actionable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
slipbox_find_similar_notesA
Find notes similar to a given note.
Similarity is based on shared tags, common links, and content overlap. Useful for discovering connections you might have missed.
Args: note_id: ID of the reference note threshold: Minimum similarity score 0.0-1.0 (default: 0.3) limit: Maximum results (default: 5)
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| note_id | Yes | ||
| threshold | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It implies a read-only operation through 'Find', but it does not explicitly state that it does not modify notes or that it is non-destructive. It does explain the similarity basis, which adds useful behavioral context, but it omits details about error behavior (e.g., missing note_id) or any side effects. The read-only nature is implied, not confirmed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured. The primary purpose is front-loaded in the first sentence, followed by a brief explanation of similarity logic and then a clear Args list. No redundant or filler content; every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read-only query tool with an output schema, the description covers the core purpose, similarity logic, and parameters. It does not mention result ordering, behavior on nonexistent note_id, or any limitations, but these are minor given the output schema exists and the tool is straightforward. The description is nearly complete; a small gap is the lack of explicit note that the note must exist or that results are returned in any particular order.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must explain parameters. It does so thoroughly in the Args section: note_id as 'ID of the reference note', threshold as 'Minimum similarity score 0.0-1.0 (default: 0.3)', and limit as 'Maximum results (default: 5)'. This adds meaning beyond the bare schema, including ranges and defaults, fully enabling correct invocation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Find notes similar to a given note' with a specific verb (Find) and resource (notes similar to a reference). It further distinguishes itself from sibling tools by specifying similarity criteria (shared tags, common links, content overlap), making it distinct from search, central, or orphaned note tools. No ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides a usage hint: 'Useful for discovering connections you might have missed.' This implies when to use it, but it does not explicitly contrast it with alternatives like slipbox_search_notes or slipbox_get_linked_notes. There is no explicit statement of when not to use this tool or what conditions select it over siblings. The guidance is implied but not explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
slipbox_get_all_tagsA
Get all tags in the Zettelkasten.
Returns alphabetically sorted list of all tags. Use this to find existing tags before creating new notes to maintain tag consistency across your knowledge base.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the burden of behavioral disclosure. It transparently states the read-only nature through the verb 'Get', discloses the alphabetical sorting behavior, and implies no side effects, which is sufficient for this simple tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is tight and front-loaded: purpose sentence, output behavior sentence, and usage guidance sentence. Every sentence contributes essential information with no redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter read-only tool with an output schema, the description is fully sufficient. It explains what is returned, the ordering, and when to use it, leaving no operational ambiguity for an agent deciding to invoke it.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters)Skip, so there is no parameter semantics to explain. The schema coverage is effectively complete for an empty properties object, and the description adds value by describing the result rather than needing to describe inputs.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb-resource pair ('Get all tags in the Zettelkasten') and defines the output as an alphabetically sorted list. This clearly distinguishes it from sibling tools that operate on notes or links rather than tags.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly advises using this tool before creating new notes to ensure tag consistency, giving a concrete when-to-use scenario. It does not explicitly name alternatives or state when not to use it, but the context is clear for a simple zero-parameter getter.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
slipbox_get_cluster_reportA
Get pending cluster analysis for structure note creation.
Clusters are groups of notes sharing tags but lacking a structure note. High-scoring clusters are good candidates for new structure notes.
Uses cached analysis by default. Set refresh=true to regenerate. Cluster analysis runs automatically via cron if configured.
Scoring factors:
Note count (7-15 is ideal, >15 is overdue)
Orphan ratio (more orphans = more urgent)
Internal link density (fewer links = needs structure)
Recency (recent activity = active domain)
Args: min_score: Minimum cluster score 0.0-1.0 (default: 0.5) limit: Maximum clusters to return (default: 5) include_notes: Include full note list per cluster (default: false) refresh: Force regeneration of cluster analysis (default: false)
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| refresh | No | ||
| min_score | No | ||
| include_notes | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses caching (default) and refresh behavior, automatic cron execution, and the scoring factors that influence results. It does not explicitly state that the operation is read-only (aside from the word 'get'), nor does it mention any auth or rate-limit requirements. Still, the disclosure of caching, refresh side effects, and scoring logic goes well beyond the schema, so a 4 is appropriate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured: a one-sentence purpose, a brief explanation, a bulleted list of scoring factors, and a clear Args block. It is not bloated, but the scoring factors could arguably be moved to a separate section without harming clarity. It is front-loaded with the core purpose, so it earns a 4.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema (which covers return values) and the fact that all four parameters are fully explained in the description, the tool is complete. The description covers purpose, behavior, scoring, and parameter semantics. No critical missing pieces like error handling or timeouts are necessary because the output schema and the tool's simple nature make them non-essential.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must fully compensate. It does: the 'Args' section explains each parameter with meaning and range (min_score 0.0-1.0), behavior (limit max clusters), and implications (include_notes, refresh). This adds significant value beyond the bare schema types and defaults, making it clear how each parameter affects the result.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a clear, specific statement: 'Get pending cluster analysis for structure note creation.' It then explains what clusters are and how they relate to structure notes, distinguishing this tool from siblings like slipbox_refresh_clusters and slipbox_create_structure_from_cluster. The verb 'get' and the resource 'cluster analysis' are unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides strong contextual guidance: it explains that high-scoring clusters are candidates for structure notes, and mentions the caching behavior and automatic cron runs. However, it does not explicitly compare this tool to siblings like slipbox_refresh_clusters or slipbox_dismiss_cluster, nor does it state when to use an alternative. The guidance is implied rather than explicit, so it earns a 4 rather than a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
slipbox_get_linked_notesA
Get notes linked to or from a specific note.
Use this to explore the knowledge graph around a note.
Directions:
outgoing: Notes this note links TO
incoming: Notes that link TO this note
both: All connected notes in either direction
Args: note_id: ID of the note to explore from direction: One of outgoing/incoming/both (default: both)
| Name | Required | Description | Default |
|---|---|---|---|
| note_id | Yes | ||
| direction | No | both |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the behavioral disclosure burden. It explains what the tool returns (linked notes), how direction affects the result set, and the default 'both' behavior. The imperative 'Get' implies a read-only operation, and no conflicting destructive behavior is suggested.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by a usage hint, a compact direction list, and an Args block. Every sentence adds useful information; there is no filler or over-elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With an output schema available and only two parameters, the description covers the behavioral purpose, parameter semantics, directions, and default value. The only minor gap is the absence of explicit guidance on when to prefer sibling graph-analysis tools, but this does not block correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so parameter documentation falls entirely on the description. It explains note_id as the starting point and direction with its allowed values and default. It could add more constraints for note_id, but the provided semantics are sufficient for a two-parameter tool.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description starts with a precise verb-resource pair: 'Get notes linked to or from a specific note.' It defines outgoing/incoming/both directions and frames the tool as knowledge-graph exploration, clearly distinguishing it from single-note retrieval or search tools like slipbox_get_note or slipbox_search_notes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
'Use this to explore the knowledge graph around a note' gives a clear context for when to call it, and the direction definitions clarify the kind of traversal intended. It does not explicitly say when not to use it or name a sibling alternative, but the intended use is evident.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
slipbox_get_noteA
Retrieve a note by ID or title.
Returns full note content including metadata, tags, and links. Use this to read note contents before creating links or updates.
Args: identifier: Either the note ID (e.g. "20251217T172432480464000") or exact title
| Name | Required | Description | Default |
|---|---|---|---|
| identifier | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden of behavioral disclosure. It effectively communicates read-only behavior and details what the response contains ('full note content including metadata, tags, and links'), which goes beyond the schema. It does not cover failure modes such as missing identifiers, but this is a minor gap for a simple retrieval tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact, front-loaded, and every line earns its place: purpose, return contents, usage guidance, and parameter meaning. No filler or irrelevant detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a one-parameter read tool with an output schema present, the description covers the essential aspects: what the tool does, how to identify the note, and when to use it. It lacks explicit not-found or error behavior, but that is not critical for normal invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate for the bare 'identifier' parameter. It does so by explaining that the identifier can be a note ID or exact title and gives a concrete ID format example. Ambiguity handling between the two forms is not addressed, but the compensation is strong for a single parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific action ('Retrieve a note') with resource and lookup method ('by ID or title'), and clarifies the result includes full content, metadata, tags, and links. This clearly differentiates it from sibling read tools like search_notes or get_linked_notes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says to use this tool 'to read note contents before creating links or updates,' which is a clear usage context tied to relevant siblings. It does not mention when NOT to use it or name alternatives, so it stops short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
slipbox_list_notes_by_dateA
List notes by creation or update date.
Useful for reviewing recent work or finding notes from a specific period.
Args: start_date: Start date in ISO format YYYY-MM-DD (optional) end_date: End date in ISO format YYYY-MM-DD (optional) use_updated: If true, filter by updated_at instead of created_at (default: false) limit: Maximum results (default: 10)
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| end_date | No | ||
| start_date | No | ||
| use_updated | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It explains filtering by created_at or updated_at, optional date bounds, and the default limit. However, it does not disclose result ordering, how date boundaries are handled, or pagination behavior, leaving some uncertainty about exact behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact, front-loaded with the primary action, and organized with a clear Args block. It is slightly redundant with the schema's property definitions, but since the schema lacks descriptions, the explicit parameter documentation is justified and easy to scan.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a read-only listing tool with an output schema, the description covers the essential invocation details and parameter semantics. It does not discuss ordering or edge cases, but the output schema and the simplicity of the operation make the definition sufficiently complete for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must fully compensate. It explains every parameter beyond the raw schema: ISO date format for start_date/end_date, the true/false semantics of use_updated, and the default limit. This is exactly the kind of parameter-level guidance an agent needs.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb and resource: 'List notes by creation or update date.' This makes the tool's purpose immediately clear and distinguishes it from content-based or similarity-based sibling tools. It also gives practical use cases, reinforcing what the tool is for.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description states when the tool is appropriate: 'Useful for reviewing recent work or finding notes from a specific period.' It provides clear context, though it does not explicitly point to alternatives such as slipbox_search_notes for content-based lookup or when not to use this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
slipbox_rebuild_indexA
Rebuild the database index from markdown files.
Use this if notes were edited outside the MCP server or if the database seems out of sync with the filesystem.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden of behavioral disclosure. It states what the tool does (rebuild index) but does not reveal any side effects, such as whether it overwrites the existing index, if it destroys data, if it requires exclusive access, or if it is safe to run concurrently. For a maintenance operation that modifies state, this is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with zero wasted words. The action is front-loaded, and the usage context is delivered in the second sentence. It is appropriately concise for a zero-parameter maintenance tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with no parameters and an output schema present, the description provides the essential trigger conditions. However, it omits behavioral details like whether the operation is destructive, if it locks the database, or if it can be run while other operations are in progress. Given no annotations, the description should disclose more about the operation's impact, so a 3 is appropriate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so according to the scoring rule the baseline is 4. The description adds no parameter-specific information, but none is needed; the schema already confirms an empty parameter set. The description's clarity about the action suffices.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('rebuild'), resource ('database index'), and source ('from markdown files'), which clearly distinguishes it from sibling tools that handle notes, links, clusters, or searches. There is no other rebuild-oriented sibling, so the purpose is unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use the tool: when notes were edited outside the MCP server or the database seems out of sync with the filesystem. It provides clear context for invocation but does not mention alternatives or exclusions, which is acceptable since this is a unique maintenance operation with no direct sibling alternative.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
slipbox_refresh_clustersA
Regenerate cluster analysis and save report.
Analyzes all notes for emergent clusters based on:
Tag co-occurrence (tags that frequently appear together)
Connection patterns (notes that link to each other)
Structure note coverage (which clusters already have structure notes)
Results saved to cluster-analysis.json next to the vault's SQLite index (or SLIPBOX_CLUSTER_REPORT_PATH when set)
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the behavioral disclosure burden. It clearly states the tool scans all notes, uses three clustering signals, and writes results to cluster-analysis.json or SLIPBOX_CLUSTER_REPORT_PATH. It does not explicitly say whether the existing report is overwritten or whether the SQLite index must exist, but the 'regenerate' and 'saved to' language covers the primary side effect.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The first sentence is a crisp summary, followed by a scannable bulleted list of analysis criteria and a clear statement of the output location. Every sentence adds necessary information with no repetition or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter command with an output schema available, the description provides the essential execution facts: what is analyzed, how clusters are formed, and where the report is written. It would be slightly stronger with an explicit pointer to slipbox_get_cluster_report for reading the saved report, but that gap does not block correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters and the empty schema is fully self-documenting, so the baseline of 4 applies. The description enhances understanding by explaining what the analysis is based on, even though there are no parameter meanings to clarify.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The opening sentence names a specific operation ('Regenerate cluster analysis and save report'), and the bulleted list defines the analysis scope. This distinguishes it from siblings like slipbox_get_cluster_report and slipbox_dismiss_cluster by focusing on regeneration and report writing.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies this should be used when refreshed cluster analysis is needed, and it states the analysis dimensions. However, it does not explicitly contrast it with slipbox_get_cluster_report for reading existing reports or mention any prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
slipbox_remove_linkB
Remove a link between two notes.
Args: source_id: ID of the source note target_id: ID of the target note bidirectional: If true, removes links in both directions
| Name | Required | Description | Default |
|---|---|---|---|
| source_id | Yes | ||
| target_id | Yes | ||
| bidirectional | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral disclosure burden. It states the removal operation and the bidirectional option, but does not disclose idempotency, behavior when the link does not exist, side effects, or whether the operation is reversible—important gaps for a destructive action.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and well-structured: one clear purpose sentence followed by a scannable argument list. There is no filler or repetition beyond the necessary parameter explanations.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple three-parameter scalar tool with an output schema, the core invocation is understandable. However, the lack of sibling differentiation and the absence of behavioral guidance around a destructive operation make the description incomplete for a fully informed tool selection.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description adds definitions for source_id, target_id, and bidirectional, which is helpful. However, the definitions are minimal and mostly restate the parameter names; they do not specify ID formats, lookup semantics, or additional constraints beyond what the schema already shows.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action and resource: 'Remove a link between two notes.' However, it does not differentiate this tool from the sibling slipbox_delete_link, which appears to have an overlapping purpose, so it stops short of full sibling-level clarity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description offers no guidance on when to use this tool instead of slipbox_delete_link or any other alternative. It only explains parameters, leaving the agent to infer the intended context from the tool name.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
slipbox_search_notesA
Search for notes by text, tags, or type.
Searches across titles and content. Combine parameters for precise filtering.
Examples:
Search by topic: query="poetry revision"
Filter by tag: tags="craft,poetry"
Find structure notes: note_type="structure"
Combined: query="metaphor" tags="poetry" limit=5
Args: query: Text to search in titles and content (optional) tags: Comma-separated tags to filter by, e.g. "poetry,craft" (optional) note_type: Filter by type: fleeting/literature/permanent/structure/hub (optional) limit: Maximum results to return (default: 10)
| Name | Required | Description | Default |
|---|---|---|---|
| tags | No | ||
| limit | No | ||
| query | No | ||
| note_type | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It does not mention any side effects, permissions, ordering, pagination, or behavior when no query is supplied (e.g., returns all notes). It only states the action, not the consequences or edge cases. This is a significant gap for a search tool that could return unexpected results if called with empty parameters.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a clear opening sentence, followed by explanatory text and a formatted Args section. It uses examples to illustrate usage without being verbose. It could be slightly more concise in the intro, but the structure is logical and front-loaded with the core purpose. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 4 optional parameters and no required ones, the description covers each parameter's semantics well. However, it omits edge-case behavior such as what happens with no parameters (does it return all notes?) or how results are ordered. Since an output schema exists, return format is covered, but behavioral edge cases are not. It's adequate for basic usage but leaves some ambiguity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must explain parameters. It does this thoroughly: query searches titles and content, tags are comma-separated, note_type lists allowed values (fleeting/literature/permanent/structure/hub), and limit has a default of 10. Examples illustrate parameter combinations. This adds substantial meaning beyond the schema's minimal type/default information.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Search for notes by text, tags, or type' and elaborates that it searches across titles and content. This is specific and distinct from sibling tools like slipbox_get_note (retrieves a single note) or slipbox_list_notes_by_date (lists by date). The verb 'search' and resource 'notes' are explicit, making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides concrete examples for each parameter combination, demonstrating when to use query, tags, note_type, and limit. It implies usage scenarios clearly. While it doesn't explicitly mention alternatives or when not to use this tool, the sibling tools have distinct purposes, so an agent can infer appropriate usage. It's strong but lacks explicit exclusion guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
slipbox_update_noteA
Update an existing note.
Only provided fields are updated; omitted fields remain unchanged. Pass empty string for tags to clear all tags. Pass empty string for references to clear all references.
Constraint: notes with note_type='literature' must have at least one reference. If you are promoting a note to 'literature', pass the citation in references in the same call.
Args: note_id: The ID of the note to update title: New title (optional) content: New content (optional) note_type: New type: fleeting/literature/permanent/structure/hub (optional) tags: New comma-separated tags, or empty string to clear (optional) references: New newline-separated citations, or empty string to clear (optional)
| Name | Required | Description | Default |
|---|---|---|---|
| tags | No | ||
| title | No | ||
| content | No | ||
| note_id | Yes | ||
| note_type | No | ||
| references | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and does well: it discloses that omitted fields remain unchanged, that empty strings clear tags/references, and that literature notes require at least one reference. It does not cover error behavior for missing note_ids or response details, but the key behavioral traits are surfaced.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well organized: behavioral notes come first, followed by a compact Args list. There is minor redundancy between the prose and the Args section regarding empty-string clearing, but no wasted sentences overall.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no schema descriptions, the description covers all six parameters plus the critical literature-reference constraint. An output schema exists, so return-value documentation is not required. It could add behavior for nonexistent notes, but the description is substantially complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the Args section fully compensates by explaining every parameter's meaning, allowed note_type values, comma-separated tags, newline-separated references, and empty-string clearing behavior. This is exactly the kind of parameter-level detail an agent needs.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Update' and the resource 'existing note', so it is unambiguous that this tool mutates an existing slipbox note. It does not explicitly differentiate from siblings like slipbox_create_note or slipbox_delete_note, but the verb+resource combination leaves little room for confusion.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description conveys when to use the tool by explaining partial-update semantics and the promotion constraint to literature notes. It does not explicitly say 'use this instead of create/delete' or provide when-not-to-use guidance, but the context is clear enough that an agent can route appropriately.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
19 tool updates
v1.5.4- Changed
slipbox_create_link1 field changed- changed
Output schema / (root)Previous value: -nullNew value: +{ + "properties": { + "result": { + "title": "Result", + "type": "string" + } + }, + "required": [ + "result" + ], + "title": "slipbox_create_linkOutput", + "type": "object" +}
- Changed
slipbox_create_note1 field changed- changed
Output schema / (root)Previous value: -nullNew value: +{ + "properties": { + "result": { + "title": "Result", + "type": "string" + } + }, + "required": [ + "result" + ], + "title": "slipbox_create_noteOutput", + "type": "object" +}
- Changed
slipbox_create_structure_from_cluster1 field changed- changed
Output schema / (root)Previous value: -nullNew value: +{ + "properties": { + "result": { + "title": "Result", + "type": "string" + } + }, + "required": [ + "result" + ], + "title": "slipbox_create_structure_from_clusterOutput", + "type": "object" +}
- Changed
slipbox_delete_link1 field changed- changed
Output schema / (root)Previous value: -nullNew value: +{ + "properties": { + "result": { + "title": "Result", + "type": "string" + } + }, + "required": [ + "result" + ], + "title": "slipbox_delete_linkOutput", + "type": "object" +}
- Changed
slipbox_delete_note1 field changed- changed
Output schema / (root)Previous value: -nullNew value: +{ + "properties": { + "result": { + "title": "Result", + "type": "string" + } + }, + "required": [ + "result" + ], + "title": "slipbox_delete_noteOutput", + "type": "object" +}
- Changed
slipbox_dismiss_cluster1 field changed- changed
Output schema / (root)Previous value: -nullNew value: +{ + "properties": { + "result": { + "title": "Result", + "type": "string" + } + }, + "required": [ + "result" + ], + "title": "slipbox_dismiss_clusterOutput", + "type": "object" +}
- Changed
slipbox_find_central_notes1 field changed- changed
Output schema / (root)Previous value: -nullNew value: +{ + "properties": { + "result": { + "title": "Result", + "type": "string" + } + }, + "required": [ + "result" + ], + "title": "slipbox_find_central_notesOutput", + "type": "object" +}
- Changed
slipbox_find_orphaned_notes1 field changed- changed
Output schema / (root)Previous value: -nullNew value: +{ + "properties": { + "result": { + "title": "Result", + "type": "string" + } + }, + "required": [ + "result" + ], + "title": "slipbox_find_orphaned_notesOutput", + "type": "object" +}
- Changed
slipbox_find_similar_notes1 field changed- changed
Output schema / (root)Previous value: -nullNew value: +{ + "properties": { + "result": { + "title": "Result", + "type": "string" + } + }, + "required": [ + "result" + ], + "title": "slipbox_find_similar_notesOutput", + "type": "object" +}
- Changed
slipbox_get_all_tags1 field changed- changed
Output schema / (root)Previous value: -nullNew value: +{ + "properties": { + "result": { + "title": "Result", + "type": "string" + } + }, + "required": [ + "result" + ], + "title": "slipbox_get_all_tagsOutput", + "type": "object" +}
- Changed
slipbox_get_cluster_report1 field changed- changed
Output schema / (root)Previous value: -nullNew value: +{ + "properties": { + "result": { + "title": "Result", + "type": "string" + } + }, + "required": [ + "result" + ], + "title": "slipbox_get_cluster_reportOutput", + "type": "object" +}
- Changed
slipbox_get_linked_notes1 field changed- changed
Output schema / (root)Previous value: -nullNew value: +{ + "properties": { + "result": { + "title": "Result", + "type": "string" + } + }, + "required": [ + "result" + ], + "title": "slipbox_get_linked_notesOutput", + "type": "object" +}
- Changed
slipbox_get_note1 field changed- changed
Output schema / (root)Previous value: -nullNew value: +{ + "properties": { + "result": { + "title": "Result", + "type": "string" + } + }, + "required": [ + "result" + ], + "title": "slipbox_get_noteOutput", + "type": "object" +}
- Changed
slipbox_list_notes_by_date1 field changed- changed
Output schema / (root)Previous value: -nullNew value: +{ + "properties": { + "result": { + "title": "Result", + "type": "string" + } + }, + "required": [ + "result" + ], + "title": "slipbox_list_notes_by_dateOutput", + "type": "object" +}
- Changed
slipbox_rebuild_index1 field changed- changed
Output schema / (root)Previous value: -nullNew value: +{ + "properties": { + "result": { + "title": "Result", + "type": "string" + } + }, + "required": [ + "result" + ], + "title": "slipbox_rebuild_indexOutput", + "type": "object" +}
- Changed
slipbox_refresh_clusters1 field changed- changed
Output schema / (root)Previous value: -nullNew value: +{ + "properties": { + "result": { + "title": "Result", + "type": "string" + } + }, + "required": [ + "result" + ], + "title": "slipbox_refresh_clustersOutput", + "type": "object" +}
- Changed
slipbox_remove_link1 field changed- changed
Output schema / (root)Previous value: -nullNew value: +{ + "properties": { + "result": { + "title": "Result", + "type": "string" + } + }, + "required": [ + "result" + ], + "title": "slipbox_remove_linkOutput", + "type": "object" +}
- Changed
slipbox_search_notes1 field changed- changed
Output schema / (root)Previous value: -nullNew value: +{ + "properties": { + "result": { + "title": "Result", + "type": "string" + } + }, + "required": [ + "result" + ], + "title": "slipbox_search_notesOutput", + "type": "object" +}
- Changed
slipbox_update_note1 field changed- changed
Output schema / (root)Previous value: -nullNew value: +{ + "properties": { + "result": { + "title": "Result", + "type": "string" + } + }, + "required": [ + "result" + ], + "title": "slipbox_update_noteOutput", + "type": "object" +}
19 tool updates
v0.1.0- First observed
slipbox_create_link - First observed
slipbox_create_note - First observed
slipbox_create_structure_from_cluster - First observed
slipbox_delete_link - First observed
slipbox_delete_note - First observed
slipbox_dismiss_cluster - First observed
slipbox_find_central_notes - First observed
slipbox_find_orphaned_notes - First observed
slipbox_find_similar_notes - First observed
slipbox_get_all_tags - First observed
slipbox_get_cluster_report - First observed
slipbox_get_linked_notes - First observed
slipbox_get_note - First observed
slipbox_list_notes_by_date - First observed
slipbox_rebuild_index - First observed
slipbox_refresh_clusters - First observed
slipbox_remove_link - First observed
slipbox_search_notes - First observed
slipbox_update_note
TDQS
Scored across 19 tools
Most tools target a distinct resource/action, but slipbox_delete_link and slipbox_remove_link are near-duplicates that differ only in error behavior, and get_cluster_report with refresh=true overlaps with refresh_clusters. The descriptions clarify the differences, but an agent could still easily select the wrong tool.
Names consistently follow a slipbox_verb_noun pattern with clear domain vocabulary. Minor inconsistencies remain: delete_link vs remove_link use different verbs for the same logical operation, and search_notes sits alongside find_similar_notes, find_central_notes, and find_orphaned_notes.
At 19 tools, this is above the typical well-scoped range, but the extra surface is justified by distinct note CRUD, link management, graph analytics, and cluster maintenance features. It feels slightly heavy rather than bloated.
The server covers note and link lifecycles, search and tag discovery, graph analysis, and cluster-driven structure creation, so core Zettelkasten workflows are supported. Minor gaps exist, such as no dedicated list-all-notes tool and link edits requiring delete/recreate, but agents can work around them.
Maintenance
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